Method and apparatus for segmenting an image in order to locate a part thereof

ABSTRACT

A method is disclosed to automatically segment 3D and higher-dimensional images into two subsets without user intervention, with no topological restriction on the solution, and in such a way that the solution is an optimal in a precisely defined optimization criterion, including an exactly defined degree of smoothness. A minimum-cut algorithm is used on a graph devised so that the optimization criterion translates into the minimization of the graph cut. The minimum cut thus found is interpreted as the segmentation with desired property.

CROSS-REFERENCE TO RELATED APPLICATIONS

Reference is made to Ser. No. 60/155,494, filed Sep. 23, 1999, which isentitled “3D and Higher-dimensional Volume Segmentation via Minimum-cutAlgorithms” and hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and apparatus for segmenting3D and higher dimensional images into two subsets in order to locate apart thereof, and in particular, to a method of segmenting without anytopological restriction on the resultant subsets.

2. Description of Related Art

A situation often occurs that a multi-dimensional array that assignsdata to each multi-dimensional slot (voxel) is given and a need arisesto partition the set of voxels into two or more subsets according to thedata.

For instance, it is well known to obtain three-dimensional arrays ofdata representing one or more physical properties at regular gridpositions within the interior of solid bodies. Such data may be obtainedby non-intrusive methods such as computed axial tomography (CAT)systems, by magnetic resonance imaging (MRI) systems, or by othernon-intrusive mechanisms such as ultrasound, positron emissiontomography (PET), emission computed tomography (ECT) and multi-modalityimaging (MMI). Each of these techniques produces a planar, grid-likearray of values for each of a succession of slices of the solid object,thus providing a three-dimensional array of such values. Typically, thesolid object is a human body or a portion thereof, although the methodis equally applicable to other natural or artificial bodies. In the caseof CAT scanning, the physical value is the coefficient of x-rayabsorption. For MRI, the physical values are the spin—spin and thespin-lattice relaxation echoes. In any event, the measured physicalvalues reflect the variations in composition, density or surfacecharacteristics of the underlying physical structures.

It is likewise known to utilize such three-dimensional arrays ofinterior physical values to generate visual images of the interiorstructures within the body. In the case of the human body, the visualimages thus produced can be used for medical purposes such asdiagnostics or for the planning of surgical procedures. In order todisplay two-dimensional images of such three-dimensional interiorstructures, however, it is necessary to locate the position of theboundary of such structure within the array of physical values. Asignificant problem in displaying such internal surfaces is, therefore,the need to segment the data samples into the various tissues. This hasbeen accomplished in the prior art by simply deciding the structure towhich each voxel belongs by comparing the data associated to the voxelto a single threshold value, or to a range of threshold values,corresponding to the physical property values associated with eachstructure or its boundary. Bones or any other tissue, for example, canbe characterized by a known range of density values to which the arrayvalues can be compared. Such simple thresholding, however, is toosusceptible to noise. That is, at the boundary, voxels with values nearthreshold can be swayed either way by a smallest noise, giving verynoisy result. What is needed is to incorporate the tendency of nearbyvoxels to belong to the same partition.

Domains of applications of segmentation other than medical applicationsinclude graphics, visualization tools, and reconstruction of 3D objects.In graphics, it is known to segment an object from an image. When thereis a sequence of image (video), it can be considered a 3D image. Thus asegmentation of moving object from a video sequence is an application of3D segmentation.

Also, the data array is not limited to 3D. Higher dimensionalapplications include four-dimensional segmentation of a temporalsequence of 3D images, such as a 3D image of beating heart.

It is important in many applications that the resultant sets of voxelsare not restricted in the number of connected component. Indeed, it isgenerally necessary to be able to automatically choose the appropriatenumber of connected components. Moreover, for a larger class ofapplications, the subsets should have no topological restrictions atall. For instance, each connected component should be allowed to have asmany holes as appropriate to fit the data. Conventional methods have atleast one of the following three shortcomings: they either i) havetopological restrictions on the solution, ii) are not guaranteed toreach the optimal solution, or iii) need user help or intervention. Somemethods presuppose the nature of the set to be found. For instance, ifarteries are expected, some methods try to find one-dimensional objectwith some thickness, making it difficult to find bifurcating arteries.An algorithm that has desirable topological properties is suggested in[O. Faugeras and R. Keriven. “Complete Dense Stereovision Using LevelSet Methods”, in Proceedings of the 5th European Conference on ComputerVision, Springer-Verlag. LNCS 1406, pp. 379–393, 1998], based on anentirely different method (Level Sets of Evolution Equations). Yet, itis a gradient-descent method with no guarantee to reach the optimal.Region Growing methods, similarly, have good topological properties, butrequire user intervention to select the regions. Moreover, no RegionGrowing method is an optimization method, that is, they are notguaranteed to give optimum solutions. Another technique described in [J.Shi and J. Malik. “Normalized cuts and image segmentation.” inProceedings of the IEEE Conference on Computer Vision and PatternRecognition 1997, pp. 731–737] uses a graph technique, whichapproximates the solution (i.e., it is not guaranteed), and perhaps doesnot have the same topological properties. The present method usessimilar technique used in other area, 2D image restoration, described in[D. M. Greig, B. T. Porteous, and A. H. Seheult. “Exact maximum aposteriori estimation for binary images.” Journal of Royal StatisticalSociety B, 51, pp. 271–279, 1989].

SUMMARY OF THE INVENTION Objects and Advantages

Accordingly, it is an object of the invention to provide a method toautomatically segment 3D and higher-dimensional images stored in thememory of a computer system into two subsets without user intervention,with no topological restriction on the solution, and in such a way thatthe solution is an optimal in a precisely defined optimizationcriterion, including an exactly defined degree of smoothness.

In accordance with this and other objects of this invention, a method ofsegmenting input data representing an image is described in order tolocate a part of the image. The input data further comprises voxels. Themethod stores a graph data structure in the memory of a computer system.The graph data structure comprises nodes and edges with weights, whereinthe nodes comprise nodes s, t, and a plurality of voxel nodes. The edgescomprise at least one edge from the node s to at least one of the voxelnodes, at least one edge from at least one of the voxel nodes to thenode t, and at least one neighbor edge from at least one of the voxelnodes to another of the voxel nodes. The method further comprises thesteps of designating one of the voxel nodes as corresponding voxel nodefor each of the voxels, setting the weights for the edges, partitioningthe nodes into at least two groups, one including the node s and anotherincluding the node t, by a minimum-cut algorithm, and partitioning thevoxels into at least two segments by assigning each of the voxels to thesegment corresponding to the group to which the corresponding voxel nodefor the voxel belongs.

BRIEF DESCRIPTION OF THE DRAWINGS

Those mentioned above and other objects and advantages of the presentinvention will become apparent by reference to the following descriptionand accompanying drawings wherein:

FIG. 1A schematically shows the first nearest neighbors of a voxel inthe case of three dimensions;

FIG. 1B schematically shows the second nearest neighbors of a voxel inthe case of three dimensions;

FIG. 1C schematically shows the third nearest neighbors of a voxel inthe case of three dimensions;

FIG. 1D schematically shows the first, second and third nearestneighbors of a voxel in the case of three dimensions;

FIG. 2 illustrates the major steps in the method of present invention;

FIG. 3A schematically shows the simplest example of the problem;

FIG. 3B shows the corresponding graph that would be used in the methodof present invention to solve the problem shown in FIG. 3A;

FIG. 4A through FIG. 4D show the four possible cut of the example graphshown in FIG. 3B;

FIG. 5 is a block diagram of an MRI system that may be used in thepresent invention.

FIG. 6 is a schematic diagram showing a conceptual organization of thegraph G in the case of three dimensions;

FIG. 7A shows the simplest example to illustrate merging;

FIG. 7B shows the corresponding graph that would be used in the methodof present invention to solve the problem shown in FIG. 7A; and

FIG. 7C shows the result of merging two nodes in the graph shown in FIG.7B.

DETAILED DESCRIPTION OF THE INVENTION

General Description

Hereafter, the dimension of the input data is denoted by DIM. For threedimensions, DIM=3.

Input

In the segmentation problem that the present invention solves, aDIM-dimensional data structure stored in the memory of a computer systemis given as the input to the method, which usually is realized as acomputer program. In medical applications, the data is typicallyacquired by non-intrusive methods such as computed axial tomography(CAT) systems, by magnetic resonance imaging (MRI) systems, or by othernon-intrusive mechanisms such as ultrasound, positron emissiontomography (PET), emission computed tomography (ECT) and multi-modalityimaging (MMI), and stored in the memory of a computer system. The datastructure will be called an “image” hereafter, and comprises voxels andneighborhood structure:

-   -   i. Each voxel has associated data, such as a number or a set of        numbers (vector). Voxels are conceptually laid out in a        DIM-dimensional configuration. For instance, a 3D (DIM=3) image        can be a simple box of size L×N×M with one voxel for each of        L×N×M possible combinations of three integers (1, n, m) for 1=1,        . . . , L, n=1, . . . , N, and m=1, . . . , M. An image can also        be a subset of such a DIM-dimensional box.    -   ii. The neighborhood structure is defined by specifying a small        set of “neighbor voxels” for each voxel, according to the        application. In other words, it is specified, among all voxels,        which voxel is neighboring which other voxels. The specification        can be given as a data stored in the memory of a computer        system, or it can be given as an implicit assumption in the        program that realizes the method of the present invention, i.e.,        the program may assume a particular arrangement of voxels. The        neighborhood structure is symmetric in the sense that if a voxel        v is a neighbor of another voxel u, then u is also a neighbor        of v. The simplest set of “first nearest neighbors” for a voxel        includes 2×DIM nearest voxels given by increasing or decreasing        one of DIM coordinate entries by 1. For instance, FIG. 1A        conceptually shows the first nearest neighbors of a voxel 11 at        coordinate (l, n, m) in a 3D image. Neighbor voxels are 12 at        (l−1, n, m), 13 at (l+1, n, m), 14 at (l, n−1, m), 15 at (l,        n+1, m), 16 at (l, n, m−1), and 17 at (l, n, m+1). The “second        nearest neighbors” are those obtained by changing two of the        coordinate entries by 1, and the 3D case is schematically shown        in FIG. 1B. Similarly, a k-th nearest neighbor of a voxel v has        k coordinate entries that are different by 1 from corresponding        entries of v. FIG. 1C shows the third nearest neighbors in the        three dimensional case, and FIG. 1D shows the first, second, and        third nearest neighbors together in the case of three        dimensions. Although such a k-th nearest neighborhood structure        is a natural choice, the application of present invention is not        limited to this class of neighborhood structures.

The method partitions the voxels into two complementary subsets S and T,or, equivalently, assigns one of two labels s or t to each voxel. Theimage will be segmented in the sense that voxels in S, to which label sis assigned, will represent the “interesting” voxels for each particularapplication, such as voxels corresponding to arteries. It is anadvantage of the method of present invention that there is notopological restriction on the resultant subsets. Moreover, our methodis completely automatic with no need for user intervention, although themethod allows the user to intervene as desired in the process to improveor correct the results of the fully automatic system. The method, whileaddressed and discussed in depth for the 3D case, can be applied bythose skilled in the art to higher or lower dimensions in astraightforward way.

The criterion as to how the image should be segmented is given bydefining a set of numbers:

-   a) For each voxel v, a number a(v).-   b) For each neighboring pair of voxels v and u, a nonnegative number    b(v, u). Note that b(v, u) and b(u, v) can be different.

Then, the criterion is that the partition shall be given so that the sum

$\begin{matrix}{{\sum\limits_{{all}\mspace{14mu} v\mspace{14mu}{in}\mspace{14mu} T}\;{a(v)}} + {\sum\;{{b\left( {v,u} \right)}\mspace{14mu}\begin{matrix}{{all}\mspace{14mu}{neighboring}\mspace{14mu}\left( {v,u} \right)} \\{{such}\mspace{14mu}{that}\mspace{14mu} v\mspace{14mu}{is}\mspace{14mu}{in}\mspace{14mu} S} \\{{and}\mspace{14mu} u\mspace{14mu}{is}\mspace{14mu}{in}\mspace{14mu} T}\end{matrix}}}} & (1)\end{matrix}$is minimum over all possible assignments.

The number a(v) represents the likelihood of v to belong to S. If a(v)is positive, v is more likely to belong to S in an assignment with aminimum sum (1). If it is negative, it is more likely to be in T in anassignment with a minimum sum (1). The number b(v, u) expresses thelikelihood of the boundary coming between v and u in such a way that vis in S and u is in T. It shall be larger if such likelihood is smaller.

As an example of how these numbers may be selected, suppose that theprobabilities

-   -   P(v), of the voxel v belonging to S; and    -   P(v, u), for neighboring voxels v and u, of v belonging to S and        u belonging to T; are known. Then one possible way is to set        a(v)=A log(2P(v)),        b(v,u)=−B log(P(v,u)),        where A and B are some positive numbers. If necessary, infinity        in the case of zero probability can be handled in various and        well-known ways, for instance by using (P(v)+h)/(1+h) instead of        P(v), where h is some small (h<<1) positive number. Some        examples of how these numbers may be selected in concrete        examples are given in the detailed description of preferred        embodiments below.        Method

FIG. 2 illustrates the major steps in the method. The main idea of themethod is to map the voxels to specially interconnected nodes in agraph. Graphs are used in the art to explain certain ideas clearly; butit is also well known in the art to implement them as a data structuresstored in a computer system that can be manipulated by a program. Oneexample of such an implementation is given later in a description of anembodiment.

A directed graph with edge weights, that is, a graph where each edge hasa nonnegative number called a weight associated to it, is created instep 21. An edge from a node v to another node u is denoted hereafter byan ordered pair (v, u). The graph contains the following:

-   -   (a) There is one node for each voxel. This type of node is        hereafter called the voxel node corresponding to the voxel, and        the voxel node corresponding to voxel v is denoted by the same        letter v.    -   (b) There also are two special nodes s and t that correspond to        the two labels s and t, respectively.    -   (c) There are edges between voxel nodes. They represent the        neighborhood structure between voxels, i.e., voxel nodes        corresponding to neighboring voxels are connected by an edge.    -   (d) For every voxel node v, there is an edge (s, v) from s to v        and an edge (v, t) from v to t.

Then, in step 22, nonnegative edge weights are assigned. For each voxelnode v, the edge (s, v) has a nonnegative weight w(s, v) and the edge(v, t) has a nonnegative weight w(v, t). These weights are selected sothat the following holds:w(s,v)−w(v,t)=a(v).  (2)

Each voxel node v is also connected to its neighbors. For each neighboru of v, there are edges (v, u) and (u, v). The edge (v, u) is assigned aweight w(v, u)=b(v, u) and the edge (u, v) is assigned a weight w(u,v)=b(u, v).

These weights are chosen so that the segmentation criterion exactlycorresponds to a condition on a cut of the graph. Here, a cut is apartition of the graph into two parts, one including s and another t, aswell known to be often defined in the art. Then, each voxel node belongsto one of the parts, either including s or t. This defines asegmentation of the image: a node that belongs to the same partition asnode s is assigned the label s, and a node that belongs to the samepartition as node t is assigned the label t. If an edge goes out fromthe part including s to the one including t, the edge is said to be“cut.” This gives the method an ability to take neighbor interactioninto account. There is one-to-one correspondence between partition ofnodes and voxels. A score of the assignment (segmentation) is given bythe sum of the edge weights that have been cut. The segmentation problemis thus mapped to a problem of finding the “minimum cut”, that is, a cutwith the minimum score.

Thus in step 23, a minimum-cut algorithm is applied to the graph. Anyvariant of minimum-cut algorithms, which are well known in the art, areknown to solve this problem in polynomial time in the number of nodesand edges in the graph. The method possesses all topological propertiesas described/required above and can be applied to graphs embedded in anydimension, not only 3D.

Finally, in step 24, voxels are segmented according to the cut of thegraph. If a voxel node belongs to the same partition as s, the voxel towhich it corresponds is assigned the label s and belongs to S.Otherwise, it is assigned the label t and belongs to T. Because of theway that the edge weights are defined, the minimum cut corresponds tothe optimal segmentation, that is, it has the minimum sum of equation(1).

Thus, the method partitions the voxels into two complementary subsets Sand T, or, equivalently, assigns one of two labels s or t to each voxel,according to the criterion stated above.

An Illustration of the Process with the Simplest Example

Before going into the description of an embodiment, an illustration ofthe process of the invention by the simplest example is in order.

FIG. 3A schematically shows the simplest example. Two voxels u and v areshown as 301 and 302. The two voxels are neighbors of each other, whichis indicated by a line segment 303 between them in the figure. Beingneighbors means that the two voxels have some tendency to belong to thesame partition upon segmentation. The corresponding graph that would beused in the method is shown in FIG. 3B. It has two voxel nodes 304 and305 corresponding to the voxels 301 and 302, denoted by the same names uand v, and two additional nodes s (306) and t (307). For each of voxelnodes 304 and 305, there is an edge from s (306). The edge from s to u,denoted by (s,u), is shown as 308. Similarly, the edge (s,v) appears inthe figure as 309. There are also two edges from u and v to t (307),i.e., (u,t) and (v,t), shown as 310 and 311. There also are edges (u,v)(312) and (v,u) (313) between voxel nodes u and v (304 and 305),representing the neighborhood structure.

The method finds a cut of the graph. A cut is a partition of the nodesinto two groups, containing node s (306) and t (307) respectively. FIG.4A through FIG. 4D show the four possible cut of the example graph shownin FIG. 3B. Two groups are shown as inside (S, containing node s (306))and outside (T, containing node t (307)) of a dashed curve. Thus in FIG.4A, only s (306) is inside and other three nodes are outside. In FIG.4B, v (305) is also inside with s (306), and so on.

Given a cut of the graph, an edge going out from S to T is said to becut. Thus, in FIG. 4A, edges (s,u) and (s,v) (308 and 309), and onlythese edges, are cut. Each edge has a non-negative number called weightassociated with it. The total score of a cut is the sum of weights ofall cut edges. Thus, in FIG. 4A, the score of the cut is w(s,u)+w(s,v),where w(s,u) denotes the weight of the edge (s,u).

Note that one of, and only one of, the edges (s,u) (308) and (u,t) (310)is always cut. In FIG. 4A and FIG. 4B, (s,u) (308) is cut and (u,t)(310) is not; in FIG. 4C and FIG. 4D, (s,u) (308) is not cut and (u,t)(310) is. It is easy to see that (s,u) (308) is cut whenever u (304)belongs to T and (u,t) (310) is cut when it belong to S. Now, since theweights of these two edges are set so thatw(s,u)−w(u,t)=a(u)(see equation (2) above,) the contribution of the two edges to the totalscore of the cut is a(u) more when u (304) belongs to T than when itbelongs to S. Similarly, the weight contribution from edges (s, v) (309)and (v, t) (311) is larger by a(v) when v (305) belongs to T than whenit is in S. Thus, compared to the state when all voxel nodes are in S,that is, the state of FIG. 4D, the sum of the weights of cut edges froms to voxel nodes and those from voxel nodes to t is larger by exactly

$\sum\limits_{{all}\mspace{14mu} x\mspace{14mu}{in}\mspace{14mu} T}\;{{a(x)}.}$Note that a(x) can be either negative or positive number, or zero, forany voxel node x, though the weights must be nonnegative. Since themethod finds the cut with the least score, a(x) should be negative if xis likely to belong to T, or positive if it is likely to belong to S,according to the local data for the voxel x.

The edges (v,u) (313) and (u,v) (312) between the voxel nodes u and v(304 and 305) are not cut when the two nodes belong to the samepartition, as in FIGS. 4A and 4D. When one of the nodes u and v (304 and305) is in S and another in T, one of the edges (v,u) (313) possible cutof the example graph shown in FIG. 3B. Two groups are shown as inside(S, containing node s (306)) and outside (T, containing node t (307)) ofa dashed curve. Thus in FIG. 4A, only s (306) is inside and other threenodes are outside. In FIG. 4B, v (305) is also inside with s (306), andso on.

Given a cut of the graph, an edge going out from S to T is said to becut. Thus, in FIG. 4A, edges (s,u) and (s,v) (308 and 309), and onlythese edges, are cut. Each edge has a non-negative number called weightassociated with it. The total score of a cut is the sum of weights ofall cut edges. Thus, in FIG. 4A, the score of the cut is w(s,u)+w(s,v),where w(s,u) denotes the weight of the edge (s,u).

Note that one of, and only one of, the edges (s,u) (308) and (u,t) (310)is always cut. In FIG. 4A and FIG. 4B, (s,u) (308) is cut and (u,t)(310) is not; in FIG. 4C and FIG. 4D, (s,u) (308) is not cut and (u,t)(310) is. It is easy to see that (s,u) (308) is cut whenever u (304)belongs to T and (u,t) (310) is cut when it belong to S. Now, since theweights of these two edges are set so thatw(s,u)−w(u,t)=a(u)(see equation (2) above,) the contribution of the two edges to the totalscore of the cut is a(u) more when u (304) belongs to T than when itbelongs to S. Similarly, the weight contribution from edges (s, v) (309)and (v, t) (311) is larger by a(v) when v (305) belongs to T than whenit is in S. Thus, compared to the state when all voxel nodes are in S,that is, the state of FIG. 4D, the sum of the weights of cut edges froms to voxel nodes and those from voxel nodes to t is larger by exactly

$\sum\limits_{{all}\mspace{14mu} x\mspace{14mu}{in}\mspace{14mu} T}\;{{a(x)}.}$Note that a(x) can be either negative or positive number, or zero, forany voxel node x, though the weights must be nonnegative. Since themethod finds the cut with the least score, a(x) should be negative if xis likely to belong to T, or positive if it is likely to belong to S,according to the local data for the voxel x.

The edges (v,u) (313) and (u,v) (312) between the voxel nodes u and v(304 and 305) are not cut when the two nodes belong to the samepartition, as in FIGS. 4A and 4D. When one of the nodes u and v (304 and305) is in S and another in T, one of the edges (v,u) (313) and (u,v)(312) is cut. In FIG. 4B, the edge (v,u) (313) is cut, that is, it goesfrom within S out to T, while the edge (u,v) (312) is not cut since itgoes from outside into S. Ordinarily, the weight for the two edges canbe set to the same value. Then, the contribution from the two edges tothe total score depends only on whether the two voxel nodes belong tothe same partition or not. If it is desirable, however, the two caseswhere u and v are separated (u in S and v in T as in FIG. 4C, or u in Tand v in S as in FIG. 4B) can have different contributions, by lettingthe weights w(u,v) and w(v,u) have different values. Since the methodlooks for a cut with smaller total score, an edge with a smaller weighttends to be cut. Thus, if these weights are set relatively large, thetwo nodes tend to stay in the same partition. This gives the method atendency to prefer a more smooth solution.

When the method finds the minimum cut, it resolves a trade-off. Thelikelihood of individual voxels to belong to either partition is givenby the number a(u) and a(v). If both u and v are more likely to belongto S or T, there is no conflict; both would belong to the samepartition, and result would be either like FIG. 4A or FIG. 4D. Or if thevoxels have no tendency to stay in the same partition, that is, if theweights w(u,v) and w(v,u) are zero, again there is no conflict and eachvoxel can belong to the partition to which it tends to belong. If,however, neither are the case, there is a trade-off to be resolved.Although it is a trivial problem in this simplest example, it is ingeneral a very difficult problem to solve.

AN EMBODIMENT OF THE INVENTION—MRI IMAGE SEGMENTATION

The method of present invention is described here in more details as maybe utilized in the segmentation of arteries from MRI. Note that othernon-intrusive imaging systems such as computed axial tomography (CAT)systems, ultrasound, positron emission tomography (PET), emissioncomputed tomography (ECT) and multi-modality imaging (MMI), can alsoutilize the present segmentation method.

A Magnetic Resonance Imaging (MRI) system examines a tissue type of thehuman body by applying a gradient magnetic field of varying intensitiesto the human body, to thereby display the arrangement of nuclear spinsof bodily tissue. That is, when a radio frequency (RF) pulse wave withina strong static magnetic field is applied to the human body, the nuclearspins of bodily tissue are excited and nuclear magnetic resonance (NMR)signals are generated when the gradient magnetic field appropriate forbodily tissue is applied. By tuning various parameters such asfrequency, sequence and encoding of the RF pulse and magnetizationangle; and measuring properties of the NMR signals such as intensity andrelaxation time; the system gathers data that can then be processed bycomputer. The data is generally applied projection reconstructiontechniques to give information about the type of bodily tissue atdifferent spatial positions.

FIG. 5 is a block diagram of an MRI system that may be used in thepresent invention. The system comprises a magnet 501, gradient coils502, RF coils 503 (the RF coils should be scaled to the anatomy ofinterest), transmitter 504, RF power amplifier 505, gradient amplifier506, receiver 507, digitizer 508, computer 509, display interface 510,scan operation interface 511, image display terminal 512, camera 513 anddata store 514. The computer 509 is programmed to acquire and segmentdata in order to find anatomies of interest in accordance with theabove-described methods.

Here, it is assumed that such a data is given in the form of a 3D datastructure, stored in the memory of a computer system, that gives a setof data that characterizes the tissue-type at each voxel. Although thisdata, which hereafter is called an MRI response, is not necessarilydescribed in simple terms, it is well known in the art how to processand generate such data. (Reference is made to [W. Orrison, J. Lewine, J.Sanders, and M. Hartshorne. Functional Brain Imaging, Mosby-Year Book,St Louis, 1995.])

It is common in the art that such MRI response at each voxel is reducedto a number such that a particular number or a range of numberscorrespond to a specific tissue type, so that a 2D array of such numbersas gray-scale intensity representing a cross-section of bodily-tissuecan be displayed on a screen to be examined by a doctor. Accordingly, itis assumed here that the MRI response is given as a number at eachvoxel. However, it may be desirable depending on application that suchMRI response at each voxel is given as more complex data such as avector that, for instance, represents responses of the tissue to morethan one parameter setting in the MRI process.

Here, as the input, the data structure is given as an L×N×M array D ofdouble precision floating-point numbers. By specifying a voxelcoordinate (l, n, m), the MRI response value D[l, n, m] can be accessed,where coordinate l runs from 1 to L, coordinate n runs from 1 to N, andcoordinate m runs from 1 to M. Here, the 3D structure of the voxelsdirectly corresponds to the physical 3D structure. The value at eachvoxel is an MRI response of the tissue at physical position (l×d₁, n×d₂,m×d₃) in some Cartesian physical coordinate system, where the physicaldistance between center of voxels to the three orthogonal directions aredenoted by d₁, d₂, and d₃. That is, the physical distance between“physical” voxel (l, n, m) and (l+1, n, m) is d₁, between (l, n, m) and(l, n+1, m) d₂, and between (l, n, m) and (l, n, m+1) d₃.

As described above, the numbers due to MRI response have directconnection to the physical process of MRI. Different tissues, such asmuscle, blood, brain matter, or bone, respond differently to MRI andyield different values. Here, a segmentation of artery is desired and itis assumed that a range (d_(min), d_(max)) of MRI response valuesignifies artery. That is, it is assumed here to be known that, if theMRI response value D[l, n, m] falls between d_(min) and d_(max), thevoxel at (l, n, m) is likely to be an artery voxel.

As the output, this embodiment will produce an L×N×M array B[l, n, m],which signifies the result of segmentation as B[l, n, m]=1 if the voxelat (l, n, m) is artery, and B[l, n, m]=0 if the voxel is non-artery.

Given the array D, a graph G is constructed. Here, an undirected graph,which is a special case of a directed graph, is used. It is well knownin the art how to manipulate data structures on a computer to realizethis end. One simple example is given below where an example minimum-cutalgorithm is described.

FIG. 6 shows a conceptual schematic of the graph organization. The setof nodes V comprises two special nodes s and t, and L×N×M voxel nodescorresponding to the voxels. The voxel node that corresponds to thevoxel at coordinate (l, n, m) is herein denoted by v[l, n, m].

The set E of edges comprises the following two kinds of edges:

-   -   a) Each node v[l, n, m] is connected to both s and t. The edge        connecting v[l, n, m] and s is denoted by e_(s)[l, n, m] and the        edge connecting v[l, n, m] to t is denoted by e_(t)[l, n, m].        Hence there are 2×L×N×M edges of this kind.    -   b) Between the voxel nodes, neighboring nodes are connected.        Here, in this example, the first nearest neighborhood system is        used. Node v[l, n, m] is connected to nodes v[l−1, n, m], v[l+1,        n, m], v[l, n−1, m], v[l, n+1, m], v[l, n, m+1], v[l, n, m+1],        except when one or more of these six neighbors do not exist        because they are out of the coordinate range, in which case only        existing ones are connected. For instance, v[1, 2, M] is        connected to v[2, 2, M], v[1, 1, M], v[1, 3, M], and v[1, 2,        M−1], since v[0, 2, M] and v[1, 2, M+1] do not exist. The edge        connecting v[l, n, m] and v[l+1, n, m] is denoted by e₁[l, n,        m]; the edge connecting v[l, n, m] and v[l, n+l, m] is denoted        by e₂[l, n, m]; and the edge connecting v[l, n, m] and v[l, n,        m+1] is denoted by e₃[l, n, m]. Then there are        3×L×N×M−(L×N+N×M+L×M) edges of this kind.

Each edge has a nonnegative weight, that is, a double precision floatingpoint number equal to or greater than zero. Hereafter the weight foredge e is denoted by w(e). For instance, edge e₃[2,3,4] that connectsv[2,3,4] and v[2,3,5] has the weight w(e₃[2,3,4]).

The weights of the edges are determined as follows.

-   -   a) The weights for the edges connecting voxel nodes to s and t        are set as        w(e _(s) [l,n,m])=(d _(max) −d _(min))²        w(e _(t) [l,n,m])=(d _(min) +d _(max)−2×D[l,n,m])².    -    The reader will note that, in (2), these weights correspond to        a(v[l, n, m])=w(e_(s)[l, n, m])−w(e_(t)[l, n, m])=4(d_(max)−D[l,        n, m]) (D[l, n, m]−d_(min)) which is 0 if D[l, n, m] equals to        d_(min) or d_(max), positive if D[l, n, m] is between dm and        d_(max), and negative if D[l, n, m] is outside of the range        [d_(min), d_(max)] Thus, according to the definition, it makes        it more likely that the voxel be classified as artery if the        value falls between d_(min) and d_(max).    -   b) The weight for the edges connecting neighboring nodes are set        as        w(e ₁ [l,n,m])=K/d ₁,        w(e ₂ [l,n,m])=K/d ₂, and        w(e ₃ [l,n,m])=K/d ₃,    -    where K is a positive constant that governs smoothness of the        segmentation. Resulting sets are smoother if K is larger. These        correspond to        b(v[l,n,m],v[l+1,n,m])=b(v[l+1,n,m],v[l,n,m])=K/d ₁,        b(v[l,n,m],v[l,n+1,m])=b(v[l,n+1,m],v[l,n,m])=K/d ₂, and        b(v[l,n,m],v[l,n,m+1])=b(v[l,n,m+1],v[l,n,m])=K/d ₃,    -    respectively.

Having set all the data needed to define a graph, a minimum-cutalgorithm is applied to the graph. A minimum-cut algorithm divides theset of nodes into two parts so that s and t are separated. This divisionis called a cut of the graph. The algorithm moreover finds such a cutwith the minimum value, where value of a cut is the sum of all theweights of edges whose ends lie in different parts according to thedivision.

Several minimum-cut algorithms are known, most of which use maximum-flowalgorithm. All these algorithms give an identical results, hence whichalgorithm to use can be decided by reasons independent from the presentinvention. Also, there are known some approximation algorithms, such asthe one described in [D. Karger. “A new approach to the minimum cutproblem”, Journal of the ACM, 43(4) 1996]. These are not guaranteed togive the minimum, but some cut that is close to the minimum. These canalso be used for the present invention, and herein called minimum-cutalgorithms. An example of minimum cut algorithm is given below.

Given the result of the minimum-cut algorithm, the output values B[l, n,m] for all l, n, and m are set as follows:

-   -   If node v [l, n, m] belongs to the same part as s, set B[l, n,        m]=1.    -   Otherwise set B[l, n, m]=0.

Although these specific edge weights are defined here for the sake ofconcreteness, it is not necessary for the present invention to set theweights of the edges in this precise way. For instance, weight for anedge between neighboring voxel nodes can be set according to thedifference of the data value D[l, n, m] between the two voxels so thatit becomes greater if the values are closer, making it less likely forthe voxels to be divided in different partitions.

A Minimum-Cut Algorithm

Here, a simple minimum-cut algorithm that may be used with the presentinvention is described. It is described as may be used for higherdimensions, although by letting the constant DIM to be 3, it can bedirectly used with the preceding embodiment.

Although it is presented in a form similar to a Pascal-like programminglanguage for clarity and concreteness, and is easy to understand by oneskilled in the art, it is meant to illustrate the algorithm, not to beused as a program as it is.

The entry point is the procedure Mincut, which should be called aftersetting the input variable Size and W as described below. The resultwould be in the output variable B when it returns. The algorithm usespush-relabel maximum-flow algorithm with global relabeling. (Referenceis made to [B. V. Cherkassky and A. V. Goldberg. “On implementingpush-relabel method for the maximum flow problem.” In Proceedings of 4thInternational Programming and Combinatorial Optimization Conference,157–171, 1995.])

Constants

-   -   DIM: Dimension of the space. In the 3D case above, DIM=3.        Types    -   type NodeCoord: An array [0 . . . DIM−1] of integer;        DIM-dimensional node coordinate.    -   type EdgeCoord: An array [0 . . . DIM] of integer; edge        coordinate.    -   type NodeQueue: A queue of NodeCoord;        Notation:    -   For a DIM-dimensional array X, the element X[vc[0], vc[1], . . .        , vc[DIM−1]] of X pointed by a NodeCoord vc is abbreviated as        X[vc]. Similarly, for a (DIM+1)-dimensional array Y, Y[ec] is a        short hand for Y[ec[0], ec[1], . . . , ec[DIM]]. Also, a pair of        an integer and a NodeCoord like (1, vc) denotes an EdgeCoord.        For instance, if i is an integer and vc is a NodeCoord, Y[i, vc]        means the element Y[i, vc[0], vc[1], . . . , vc[DIM−1]].    -   In the algorithm, NodeCoord vc with vc[0]=−1 signifies the node        s, and vc[0]=−2 means the node t.    -   A control structure for all vc do . . . is used to mean to        iterate for all NodeCoord in the DIM-dimensional hypercube        specified by Size (an input variable defined below.) For        instance, in the 3D example above, this means to iterate for all        L×N×M combination of coordinates.        Auxiliary Routines    -   procedure Enqueue(vc: NodeCoord; var Q: NodeQueue); pushes vc in        the back of the queue Q.    -   function Dequeue(var Q: NodeQueue): NodeCoord; pops a NodeCoord        vc from the queue Q and return vc.    -   function Empty(var Q: NodeQueue): bool; returns true if the        queue Q is empty.        Input    -   Size: NodeCoord; The size of the voxel array. DIM-dimensional        vector.    -   W: (DIM+1)-dimensional array of double        -   W[0, vc]: weight of edge from s to vc        -   W[1, vc]: weight of edge from vc to t        -   W[1+d, vc]: weight of the edge from vc to the neighbor in            d-th dimension (the node with the d-th coordinate+1.) In the            3D case,        -   W[0, l, n, m]=w(e_(s)[l,n,m]),        -   W[1, l, n, m]=w(e_(t)[l,n,m]),        -   W[2, l, n, m]=w(e₁[l,n,m]),        -   W[3, l, n, m]=w(e₂[l,n,m]),        -   W[4, l, n, m]=w(e₃[l,n,m])            Output    -   B: DIM-dimensional array of integer. B[vc]=0 if the voxel at vc        belongs to the same partition as s.        Global Variables

In addition to Size, W, and B above, global variables are as follows:

-   -   es, et: double precision floating point number (double)        variables    -   ds, dt, NUMNODE: integer variables    -   curEdgeS, curEdgeT: NodeCoord variables    -   excess: a DIM-dimensional array of doubles. One entry for each        voxel.    -   dist, curEdge: DIM-dimensional arrays of integers. One entry for        each voxel.    -   F: a (DIM+1)-dimensional array of doubles. The same size as W.    -   Q: NodeQueue;    -   s: NodeCoord. s[0]=−1.    -   t: NodeCoord. t[0]=−2.        The Algorithm

function Excess(vc : NodeCoord) : double; begin if vc[0] =−1 then Excess← es { node s } else if vc[0] =−2 then Excess ← et { node t } elseExcess ← excess[vc]; end procedure SetExcess(vc : NodeCoord; e :double); begin if vc[0] =−1 then es ← e { node s } else if vc[0] =−2then et ← e { node t } else excess[vc] ← e; end function Distance(vc :NodeCoord) : integer; begin if vc[0] =−1 then Distance ← ds { node s }else if vc[0] =−2 then Distance ← dt { node t } else Distance ←dist(vc); end procedure SetDistance(vc : NodeCoord; d : integer); beginif vc[0] =−1 then ds ← d { node s } else if vc[0] =−2 then dt ← d { nodet } else dist(vc) ← d; end function Active(vc : NodeCoord) : bool; beginif vc[0] < 0 then Active ← false { node s or t} else if (Distance(vc) ≧0) and (Excess(vc) > 0) then Active ← true else Active ← false; endfunction Inc(var vc : NodeCoord) : bool;  {Increment for aDIM-dimensional iteration.} var   d: integer; result : bool; beginresult ← false; d← 0; while (d < DIM) and (result = false) do beginvc[d] ← vc[d] + 1; if vc[d] < Size[d] then result ← true else beginvc[d] ← 0; d ← d + 1; end; end; Inc ← result; end procedure Reset(var vc: NodeCoord); var   d : integer; begin for d ← 0 to DIM − do vc[d] ← 0;end function GetEdge(vc : NodeCoord; var wc : NodeCoord; var ec :EdgeCoord) : integer; begin if vc[0] ≧ 0 then begin { node other than sor t.} ec ← (0, vc); wc ← vc; if curEdge[vc] = 0 then begin ec[0] ← 0;wc ← s; result ← −1; end else if curEdge[vc] = 1 then begin ec[0] ← 1;wc ← t; result ← 1; end else begin if curEdge[vc] < 2 + DIM then beginec[0] ← curEdge[vc]; result ← 1; end else begin ec[0] ← curEdge[vc] −DIM; ec[ec[0] − 1] ← ec[ec[0] − 1] − 1; result ← −1; end; wc[ec[0] − 2]← wc[ec[0] − 2] + result; end; end else if vc[0] =−1 then begin ec ← (0,curEdgeS[d]); wc ← curEdgeS; result ← 1; end else begin ec ← (1,curEdgeT[d]); wc ← curEdgeT; result ← −1; end; GetEdge ← result; end;function ProceedEdge(vc : NodeCoord) : bool; var   i : integer; result,loopout : bool; begin result ← true; if vc[0] ≧ 0 then repeat beginloopout ← true; curEdge[vc] ← curEdge[vc] + 1; if curEdge[vc] = 2 + 2 ×DIM then begin curEdge[vc] ← 0; result ← false; end else ifcurEdge[vc] > 1 then begin i ← curEdge[vc] − 2; if ((i < DIM) and (vc[i]≧ Size[i] − 1)) or ((i ≧ DIM) and (vc[i − DIM] = 0)) then loopout ←false; end; end until loopout = true else if vc[0] =−1 then result ←Inc(curEdgeS) else result ← Inc(curEdgeT); ProceedEdge ← result; end;procedure ResetEdge(vc : NodeCoord); var   i : integer; begin if vc[0] ≧0 then curEdge[vc] ← 0 else if vc[0] =−1 then Reset(curEdgeS) elseReset(curEdgeT); end procedure Relabel(vc : NodeCoord); var   d, dw,mdw, r : integer; rf : double; wc : NodeCoord; ec : EdgeCoord; escape :bool begin if Active(vc) = true then begin d ← Distance(vc); mdw← −1;escape ← false; repeat begin r ← GetEdge(vc, wc, ec); rf ← W[ec] − r ×F[ec]; dw ← Distance(wc); if (rf> 0) and (d> dw) then beginResetEdge(vc); escape = true; else if rf> 0 then begin if mdw=−1 thenmdw ← dw + 1 else mdw ← min(mdw, dw + 1); end; end until (escape = true)or (ProceedEdge(vc) = false); SetDistance(vc, mdw); end; end procedurePushRelabel(vc : NodeCoord); var   r : integer; delta, rf : double; wc :NodeCoord; ec : EdgeCoord; wactive : bool begin NodeCoord wc; EdgeCoordec; r ← GetEdge(vc, wc, ec); wactive ← Active(wc); if (Active(vc) =true) and (Distance(vc) = Distance(wc) + 1) then begin rf ← W[ec] − r ×F[ec]; if rf> 0 then begin delta ← min(Excess(vc), rf); F[ec] ← F[ec] +r × delta; SetExcess(vc, Excess(vc) − delta); SetExcess(wc, Excess(wc) +delta); end else if ProceedEdge(vc) = false then Relabel(vc); end elseif ProceedEdge(vc) = false then Relabel(vc); if (wactive = false) and(Active(wc) = true) then Enqueue(wc, Q); end procedure Initialize; var  i : integer; w : double; vc : NodeCoord; begin Reset(curEdgeS);Reset(curEdgeT); NUMNODE ← 1; for i ← 0 to DIM − 1 do NUMNODE ← NUMNODE× Size[i]; NUMNODE ← NUMNODE + 2; ds ← NUMNODE; dt ← 0; es ← 0; et ← 0;for all vc do begin w ← W[0, vc]; F[0, vc] ← w; if w > 0 thenEnqueue(vc, Q); excess[vc] ← w; dist(vc) ← 0; curEdge[vc] ← 0; for i ← 1to DIM + 1 do F[i, vc] ← 0; end; end procedure BFS(root : NodeCoord;initdist : integer); {Breadth first search in residual graph.} var   d,r, dw : integer; rf : double; vc, wc : NodeCoord; ec : EdgeCoord; Q2 :Node- Queue; begin SetDistance(root, initdist); Enqueue(root, Q2); whileEmpty(Q2) = false do begin vc ← Dequeue(Q2); ResetEdge(vc); d ←Distance(vc); repeat begin r ← GetEdge(vc, wc, ec); dw ← Distance(wc);if dw =−1 then begin rf ← W[ec] + r × F[ec]; if rf> 0 then beginSetDistance(wc, d + 1); Enqueue(wc, Q2); end; end; end until(ProceedEdge(vc) = false); end; end procedure GlobalRelabel; var   vc :NodeCoord; begin for all vc do SetDistance(vc, −1); BFS(t, 0); BFS(s,NUMNODE); end procedure FindCut; var   vc : NodeCoord; begin for all vcdo SetDistance(vc, −1); BFS(t, 0); for all vc do if Distance(vc) =−1then B(vc) ← true else B(vc) ← false; end procedure Maxflow; var   vc :NodeCoord; c : integer; begin Initialize; while Empty(Q) = false dobegin vc ← Dequeue(Q); d ← Distance(vc); repeat PushRelabel(vc) until(Excess(vc) = 0) or (Distance(vc) ≠ d); if Active(vc) = true thenEnqueue(vc, Q); c ← c + 1; if c > NUMNODE then begin c ← 0;GlobalRelabel; end; end; end procedure Mincut; var   vc : NodeCoord; c :integer; begin Maxflow; FindCut; end

RAMIFICATIONS AND SCOPE

While only certain preferred features of the invention have beenillustrated and described herein, many modifications and changes willoccur to those skilled in the art.

For instance, weight for an edge between neighboring voxel nodes can beset according to the difference of the data value between the two voxelsso that it becomes greater if the values are closer to each other,making it less likely for the voxels to be divided in differentpartitions.

As mentioned above, the method of present invention can use eitherdirected or undirected graph. According to the application and thespecific implementation of the data structures, either can be moreconvenient than the other.

Also, there are known ways to handle specific cases more efficiently.First, if there is any edge with a zero weight in the graph, the edgecan be removed without affecting the result of the segmentation. Second,if there is a pair of nodes that are very strongly connected, that is,if the nodes are connected by either (a) edges with very large weightsin both directions in the directed case, or (b) by an undirected edgewith a very large weight in the undirected case, these nodes will neverbe separated in the minimum-cut algorithm. Thus, it is possible to mergethese two nodes into one node to improve the efficiency. These are wellknown in the art as pre-processes of minimum-cut algorithms. In such acase, there may be fewer voxel nodes than input voxels. This can be seenas corresponding voxel nodes being designated to each input voxel. Inthe embodiment above, the designation is a simple one to onecorrespondence. It can be, however, such that more than one voxel havethe same designated voxel node. This simply means two voxels that hasthe same designated corresponding voxel node cannot be separated. Afterthe minimum cut is found, i.e., the nodes are partitioned into groups,the segmentation of the voxels can be readily found by assigning eachvoxel to the segment corresponding to the group to which the voxel'scorresponding voxel node belongs.

FIG. 7A shows the simplest example to illustrate this. There are threevoxels, u,v, and w. Each voxel is neighbor of other two. Suppose thevoxels u and w are known to have so strong a tendency to stay in thesame segment that it can be assume they never can be separated. Theundirected version of the method of present invention would usually givethe graph shown in FIG. 7B. However, it is known that the graph shown inFIG. 7C would give an equivalent solution with fewer nodes and edges.Here, the nodes u and w are merged to one node 71. The edges connectings, t, or v and u and w are also merged and their weights added together.Thus, instead of edges (s,u) and (s,w) in FIG. 7B, there is only oneedge from s to node 71 in FIG. 7C with a weight equal to the sum ofweights of the original edges. In this example, node 71 is designated asthe voxel node corresponding to both u and w. Node 72 is, on the otherhand, designated as the voxel node corresponding to only one voxel,which is v, as in the ordinary case. By repeating this, it is possibleto merge more than one node. The weight of an edge connecting some nodex and a merged node y is given by the sum of weights of all edges thatconnect node x and all nodes that are merged into y. In the context ofthe present invention, this means that the equation (2) becomes, for avoxel node v,

${{w\left( {s,v} \right)} - {w\left( {v,t} \right)}} = {\sum\limits_{{all}\mspace{14mu} x\mspace{14mu}{corresponding}\mspace{14mu}{to}\mspace{14mu} v}\;{{a(x)}.}}$

Note if, as in the embodiment above, each voxel node corresponds toexactly one voxel, this equation reverts to (2). This is nothing but anobvious result of applying the merging to the present context. Thisprocess of merging is well known in the art and its addition should notbe considered to bring the resultant method out of the scope of thepresent invention.

Finally, as mentioned above, there are known more than one minimum-cutalgorithms. Also, there are approximation algorithms that giveapproximate minimum cut. Moreover, some approximation algorithms cangive so-called multiway-cut that partitions the graph into more than twogroups. Such algorithms however can be easily incorporated into thescheme of the present invention by those skilled in the art.

It is, therefore, to be understood that the appended claims are intendedto cover all such modifications and changes as fall within the truespirit of the invention.

1. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point; a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point; and a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information, wherein a connection between the at least one first point and the at least one second point provides a particular weight which is indicative of the at least one first point belonging with the at least one second point.
 2. The software storage medium of claim 1, wherein the particular weight influences whether the at least one first point can be placed separately into the first part from the at least one second point.
 3. The software storage medium of claim 1, wherein the third module, when executed, associates the first and second points by determining whether the at least one first point is to be associated with the first part made based on the first weight and a particular weight provided by a connection between the at least one first point and the at least one second point.
 4. The software storage medium of claim 3, wherein the third module, when executed, associates the first and second points by determining whether the at least one second point is to be associated with the second part made based on the second weight and the particular weight.
 5. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point; a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point; and a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information, wherein the first information includes first weights, and wherein the second information includes second weights, and wherein the at least one first point is associated with the first part if the first weight is greater than a predetermined threshold.
 6. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point; a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point; and a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information, wherein the first information includes first weights, and wherein the second information includes second weights, and wherein the at least one second point is associated with the second part if the second weight is greater than a predetermined threshold.
 7. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point; a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point; and a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information, wherein the first information includes first weights, and wherein the second information includes second weights, wherein a connection between the at least one first point and the at least one second point provides a third weight which is indicative of the at least one first point belonging with the at least one second point, wherein the at least one first point is associated with the first part if the first weight is greater than a first threshold, wherein the at least one second point is associated with the second part if the second weight is greater than a second threshold, and wherein the third module, when executed, associates the first and second points by (i) determining whether the at least one first point is to be associated with the first part made based on the first weight and the third weight, and (ii) determining whether the at least one second point is to be associated with the second part made based on the second weight and the third weight.
 8. The software storage medium of claim 7, wherein a first possibility is a possibility that the first and second points are associated with the first part, wherein a second possibility is a possibility that the first and second points are associated with the second part, wherein a third possibility is a possibility that the at least one first point is associated with the first part, and the at least one second point is associated with the second part, and wherein a fourth possibility is a possibility that the at least one first point is associated with the second part, and the at least one second point is associated with the first part.
 9. The software storage medium of claim 8, wherein the first possibility is associated with a first cost data which is obtained by adding the first and second thresholds, wherein the second possibility is associated with a second cost data which is obtained by adding the first and second weights, wherein the third possibility is associated with a third cost data which is obtained by adding the first threshold, the second weight and the third weight, and wherein the fourth possibility is associated with a fourth cost data which is obtained by adding the first weight, the second thresholds and the third weight.
 10. The software storage medium of claim 9, wherein the first possibility is prevalent when the first cost data is smaller than the second, third and fourth cost data, wherein the second possibility is prevalent when the second cost data is smaller than the first, third and fourth cost data, wherein the third possibility is prevalent when the third cost data is smaller than the first, second and fourth cost data, and wherein the fourth possibility is prevalent when the fourth cost data is smaller than the first, second and third cost data.
 11. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point; a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point; and a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information, wherein the first data and the second data form a graph data structure which comprises nodes and edges.
 12. The software storage medium of claim 11, further comprising: a fourth module which, when executed, assigns weights for the edges between the first and second points, wherein a first one of the weights is provided for a first edge of the edges connecting the first point with a voxel node to a first nonnegative number, and wherein a second one of the weights is provided for a second edge of the edges connecting the voxel node with the second point to a second nonnegative number.
 13. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point; a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point; a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information; and a fourth module which, when executed, receives third data corresponding to at least one third point in the space, the third data for each one of the at least one third point including third information indicative of a likelihood of an association of the third data to at least a third part of the respective third point, wherein the associating step includes the substep of associating the at least one third point with the first and second points based on the first, second and third information.
 14. A software storage medium which, when executed by a processing arrangement, is configured to segment input data representing an image in order to locate a part of said image, said input data comprising voxels, said software storage medium comprising: a software program including: a first module which, when executed, stores a graph data structure in the memory arrangement, said graph data structure comprising nodes and edges having weights, wherein (i) said nodes comprise at least one first node s, at least one second node t, and a plurality of voxel nodes, and (ii) said edges comprise: (A) at least one first edge connecting said first node s to at least one of said voxel nodes, (B) at least one second edge connecting at least one of said voxel nodes to said second node t, and (C) at least one neighbor edge connecting at least one of said voxel nodes to another one of said voxel nodes; a second module which, when executed, designates one of said voxel nodes as corresponding voxel node for each of said voxels, a third module which, when executed, partitions said nodes into at least two groups, one including said first node s and another one including said second node t, by a minimum-cut algorithm, and a fourth module which, when executed, partitions said voxels into at least two segments by assigning each of said voxels to the segment corresponding to the group to which said corresponding voxel node for the voxel belongs.
 15. The software storage medium of claim 14 wherein: said input data further comprises a neighborhood structure, and at least one of said neighbor edges is between two of said voxel nodes designated as said corresponding voxel nodes for said two of said voxels that are neighbors to one another according to said neighborhood structure.
 16. The software storage medium of claim 15, wherein said voxels comprise at least one of a portion of or an entire DIM-dimensional array of data.
 17. The software storage medium of claim 16, wherein said neighborhood structure comprises a k-th nearest neighborhood structure.
 18. The software storage medium of claim 17, wherein at least one part of said array of data represents one or more physical properties of said voxels at regular grid positions within an interior of solid bodies.
 19. The software storage medium of claim 16, wherein at least one part of said array of data represents one or more physical properties of said voxels at regular grid positions within an interior of solid bodies.
 20. The software storage medium of claim 16, wherein a size of the DIM is at least
 3. 21. The software storage medium according to claim 14, wherein the first module segments said nodes and said edges, and wherein the third module implements said minimum-cut algorithm on said segmented nodes and edges.
 22. The software storage medium of claim 14, wherein said input data further comprises a likelihood number for each of said voxels, and further comprising: a fourth module which, when executed, sets weights for said first, second and neighbor edges for each voxel node by setting: (i) a weight for said first edge connecting said first node s with said voxel node to a first nonnegative number w1, and (ii) a weight for said second edge connecting said voxel node with said second node t to a second nonnegative number w2 so that the first non-negative number minus the second non-negative number equals the sum of likelihood numbers for all of said voxels to which said voxel node is designated as said corresponding voxel node.
 23. The software storage medium of claim 22, wherein: said input data further comprises a neighborhood structure, and at least one of said neighbor edges is between two of said voxel nodes designated as said corresponding voxel nodes for two of said voxels that are neighbors according to said neighborhood structure.
 24. The software storage medium of claim 23, wherein said voxels comprise at least one of a portion of or an entire a DIM-dimensional array of data.
 25. The software storage medium of claim 24, wherein said neighborhood structure comprises a k-th nearest neighborhood structure.
 26. The software storage medium of claim 25, wherein at least one part of said array of data represents one or more physical properties at regular grid positions within an interior of solid bodies.
 27. The software storage medium of claim 24, wherein at least one part of said array of data represents one or more physical properties at regular grid positions within an interior of solid bodies.
 28. The software storage medium of claim 24, wherein a size of the DIM is at least three.
 29. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point, a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point, and a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information, wherein the first and second points are connected to one another via edges so as to form a graph structure.
 30. The software storage medium of claim 29, wherein the first data corresponds to the a first voxel, and wherein the second data corresponds to second voxel.
 31. The software storage medium of claim 29, wherein the first information includes first weights, and wherein the second information includes second weights.
 32. The software storage medium of claim 31, wherein the first point is associated with the first part if the first weight indicates a higher likelihood for such association.
 33. The software storage medium of claim 31, wherein the second point is associated with the second part if the second weight indicates a higher likelihood for such association.
 34. A software storage medium which, when executed by a processing arrangement, is configured to associate particular data in a space which has at least three dimensions, the software storage medium comprising: a software program including: a first module which, when executed, receives first data corresponding to at least one first point in the space, the first data for each one of the at least one first point including first information indicative of a likelihood of an association of the first data with at least a first part of the respective first point, a second module which, when executed, receives second data corresponding to at least one second point in the space, the second data for each one of the at least one second point including second information indicative of a likelihood of an association of the second data with at least a second part of the respective second point, and a third module which, when executed, associates the first and second points to the respective first and second parts based on the first and second information, wherein the first data and the second data form a graph data structure which comprises nodes and edges. 